Here, we argue that a better technique is to start and end without any data augmentation (or only weak augmentation) and to use an increasing and then decreasing strength of augmentation in the middle epochs. This follows naturally from the premise of this paper: to ease and encourage learning in the earliest iterations or epochs, then gradually to increase the span of the problem space of the network's learning as the training progresses. This implies starting with no or only very weak augmentation (i.e., image flips) and gradually incorporating stronger augmentation as the training progresses. In addition, as one approaches the end of the training, it is intuitive to eliminate the strong data augmentation and to fine-tune in the final epochs on the original training data to encourage learning of more prototypical patterns [1]}.
